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OPEN The efect of the VKORC1 promoter variant on warfarin responsiveness in the Saudi WArfarin Pharmacogenetic (SWAP) cohort Maha Al Ammari1, Mohammed AlBalwi 2, Khizra Sultana3, Ibrahim B. Alabdulkareem4, Bader Almuzzaini 5, Nada S. Almakhlaf1, Mohammed Aldrees5 & Jahad Alghamdi 6*

Warfarin is a frequently prescribed oral anticoagulant with a narrow therapeutic index, requiring careful dosing and monitoring. However, patients respond with signifcant inter-individual variability in terms of the dose and responsiveness of warfarin, attributed to genetic polymorphisms within the genes responsible for the pharmacokinetics and pharmacodynamics of warfarin. Extensive warfarin pharmacogenetic studies have been conducted, including studies resulting in genotype-guided dosing guidelines, but few large scale studies have been conducted with the Saudi population. In this study, we report the study design and baseline characteristics of the Saudi WArfarin Pharmacogenomics (SWAP) cohort, as well as the association of the VKORC1 promoter variants with the warfarin dose and the time to a stable INR. In the 936 Saudi patients recruited in the SWAP study, the minor allele C of rs9923231 was signifcantly associated with a 8.45 mg higher weekly warfarin dose (p value = 4.0 × 10–46), as well as with a signifcant delay in achieving a stable INR level. The addition of the rs9923231 status to the model, containing all the signifcant clinical variables, doubled the warfarin dose explained variance to 31%. The SWAP cohort represents a valuable resource for future research with the objective of identifying rare and prevalent genetic variants, which can be incorporated in personalized anticoagulation therapy for the Saudi population.

Warfarin is a frequently prescribed oral anticoagulant agent inhibiting the synthesis of the Vitamin K dependent clotting factors II, VII, IX, X as well as anticoagulation factors protein C and S. It also causes anticoagulation by inhibiting the Vitamin K-epoxide reductase complex, which result in the accumulation of reduced vitamin K2, 3-epoxide and vitamin K hydroquinone­ 1. Warfarin is indicated for the treatment or prophylaxis of patients with various disease states, including venous thromboembolic events­ 2, atrial fbrillation and futter­ 3, cardiac surgery for valve replacement and orthopedic ­surgery4,5. Warfarin exhibits a narrow therapeutic index and is monitored through the International Normalized Ratio (INR), which measures the ability of the blood to clot. Te optimal efect of warfarin, for most of the indications, is when the INR ranges from 2 to 3, except for metallic atrial valve replacement, in which a higher INR value is required­ 6. Several studies reported ethnic diferences in the warfarin response and dosage requirements­ 7–9. Even for populations with very close ancestry within the Middle East and North Africa (MENA) region, diferences in

1Pharmaceutical Care Services, King Abdulaziz Medical City, Ministry of National Guard Health Afairs, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. 2Department of Pathology and Laboratory, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Afairs, Riyadh, Saudi Arabia. 3Research Ofce, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Afairs, Riyadh, Saudi Arabia. 4Health Sciences Research Center, King Abdullah Bin Abdulaziz University Hospital, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. 5Medical Genomics Research Department, King Abdullah International Medical Research Center, Ministry of National Guard Health Afairs, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. 6King Abdullah International Medical Research Center, The Saudi Biobank, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Afairs, Riyadh, Saudi Arabia. *email: [email protected]

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allelic frequencies and their impact on warfarin dosing has been ­documented10. Tis wide inter-individual vari- ability in the warfarin dose is attributed to several factors, including allelic diferences of genetic variants in the warfarin metabolizing enzyme, and environmental factors within each ­population11,12. Genetic variants within cytochrome P450 2C9 (CYP2C9) and the vitamin K-epoxide reductase complex (VKORC1) enzyme explain approximately 50% of the dose ­variability13. Most of the pharmacogenetic guidelines consider variants within two genes, CYP2C9*2, CYP2C9*3, and VKORC1:c.−1639C>T (rs9923231)14. Te diferences in the percentage of variance in the warfarin dose explained by VKORC1 in a population are, to a large degree, due to rs9923231­ 9. Tis variant is signifcantly associated with the warfarin dose in several populations, including Indians, Chinese, Brazilian, Turkish, Russian, and Emiratis­ 15–20. All the studies indicated a signifcant infuence on the warfarin response, accounting for 11–32% of the variability in the warfarin dose­ 9,15–20. However, few studies with the MENA populations, and particularly the Saudi population, have been conducted to investigate the pharma- cogenetic interaction of warfarin responsiveness. Establishing a large cohort of warfarin users would provide opportunities to identify novel risk alleles for drug responses and prevalent diseases, especially with the high consanguinity rate within the Saudi population­ 21. Tus, the aim of this study was to report the cohort profle of the Saudi WArfarin Pharmacogenomics (SWAP), as a large prospective cohort for warfarin pharmacogenomic studies, and to assess the value of this cohort by reporting the association of rs9923231 to warfarin efectiveness. Methods Study design. Tis study was a prospective multi-center cohort of patients admitted to the King Abdulaziz Medical City, Ministry of National Guard-Health Afairs (MNGHA), in Riyadh and Jeddah, and King Khalid University Hospital in Riyadh. Te study was approved by the Institutional Review Board of King Abdullah International Medical Research Center (KAIMRC) with grant number RC12/163, in accordance with the Hel- sinki Declaration of 1975. All patients provided written informed consent.

Study participants. Te study enrolled patients from January 2014 to April 2018 from three centers in Riyadh and Jeddah, Saudi Arabia. All patients were Saudi nationals, older than 18 years, and for whom warfarin therapy was initiated or patients using warfarin with an INR 2–3. Patients were excluded if their baseline anti- coagulation profle was prolonged (INR more than 1.5), aPTT > 1.5–2 times the normal value, baseline bilirubin more than 2.4 gm/dl, had a mechanical heart valve replacement that requires the INR target to be above 3, receiving chemotherapy, and had a confrmed diagnosis of HIV or hepatitis A, B, C. An EDTA peripheral blood sample was drawn for genetic analysis and the clinical variables were retrieved from the hospital’s electronic records system during the hospital admission. Te data included demographic and anthropometric variables such as age, gender, weight, and height. Te clinical data included comorbidities, renal, liver, and thyroid func- tions, drug interactions, hemoglobin levels and the anticoagulation profle. In this study, patients were followed- up for 10 days only, regardless of whether the participant was admitted or an outpatient. All the variables were compiled in an electronic database for subsequent analysis.

DNA extraction and genotyping assay. All patients signed a written informed consent before peripheral blood samples were collected. Genomic DNA was extracted from the peripheral blood using a Qiagen DNeasy kit (Qiagen, Germany) (spin-column protocol) following the manufacturer’s protocol. In brief, the whole blood sample was lysed using a lysis bufer, the DNA was bonded through a silica binding column and eluted through a similar column using an elution bufer. DNA purity and quantity was assessed using the Nanodrop™ spectro- photometer 8,000 and BR Qubit 3 fuorometer respectively, high quality DNA samples are used for genotyping of rs9923231 (VKOR1C −1639 C>T) using a ready-made fuorescence probe assay (C_30403261_20) by Termo Fisher Scientifc, MA, USA. All the genotyping reactions were done with the TaqMan genotyping master mix (Lot No. 0074819). Te genotyping procedure was performed following the standard protocol. In brief, a total volume of 25 µl was added in each well of the 96-well plates, as well as 1.25 µl of genotyping master mix, 1.25 µl of genotyping assay, 9.25 µl of nuclease-free water and 2 µl of 20 ng/µl of DNA. Te amplifcation protocol started with a pre-read stage at 60 °C/30 s followed by a hold stage at 95 °C/10 min; the PCR stage is 95 °C/15 s followed by 60 °C/1 min for 50 cycles. Te fnal stage is a post-read stage at 60 °C/30 s. Reporter dyes for qPCR were detected and analyzed by a real-time qPCR ABI QuantStodio 6 FLEX system with TaqMan Genotyper Sofware.

Statistical analysis. Continuous variables are summarized as median with interquartile ranges (IQR), and categorical variables as total number and percentage. Te allelic frequency of the rs9923231 summary was done in PLINK to calculate the minor allele frequency (MAF) and Hardy–Weinberg Equilibrium p ­value22. A stepwise regression model was used to select signifcant non-genetic factors by choosing a p value threshold of 0.25 for entry and 0.1 for exiting the model­ 23. Only factors with a signifcant p value < 0.05 were kept in the non-genetic model. Te genetic model included the same variables, plus the rs9923231 genotype as coded by 0, 1, and 2 for the genotypes TT, CT, and CC, respectively. Te ­R2 and adjusted ­R2 were calculated for the two models. A linear regression model was used to test for the association of rs9923231 with the weekly average dose afer adjusting for age, gender, body mass index and smoking status. Te average daily dose was calculated as the average warfa- rin dose from day 3 to day 10. Te Cox regression Hazard model has been adopted by several studies to evaluate the efect of genotypes on the time required to reach a stable INR­ 24,25. In this study, a Cox regression model was performed to determine the hazard ratio (HR) for achieving the target INR, and a Kaplan–Meier Survival curve was plotted for the diferent genotypes and log-rank statistics were computed to test the homogeneity of the estimated survival function for the ­groups25.

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Figure 1. Flow chart of the participants screening step.

Results Baseline characteristics. During the study recruitment period, a total of 2,581 patients were screened as potential participants in the study (Fig. 1). Of this group, 936 patients fulflled the inclusion criteria and included in the study. Te sample was classifed according to the warfarin status on the day of recruitment, to either naïve patients (n = 602), who have never been prescribed warfarin before, or to ex-user (n = 334), who were already on warfarin treatment on the day of recruitment. Te baseline characteristics of the sample are displayed in Tables 1 and 2. Te average age was 62.4 ± 18.9 years, and 55.2% were female. Te most prevalent indications for warfarin were atrial fbrillation (AF, 44.7%), pulmonary embolism (PE, 18.7%), and deep vein thrombosis (DVT, 18.3%). Te most frequent comorbidities were hypertension (75%), diabetes (54%), and hyperlipidemia (42%).

Genotyping rs9923231. Te minor allele frequency of rs9923231 allele C was 0.43, and the genotypic fre- quencies were 0.21, 0.45, and 0.34 for genotypes CC, CT, and TT, respectively (Table 3). Te genotypic frequency was not signifcantly diferent between the groups on initiation or in the maintenance phase (p value = 0.27). Te average weekly dose of warfarin was signifcantly diferent between the three genotypes (p value = 1.81E-40), with 35.28 mg (95% CI 33.48–37.08), 25.45 mg (95% CI 24.23–26.66), and 18.73 mg (95% CI 17.33–20.14) for the groups of CC, CT, and TT carriers, respectively (Fig. 1). In addition, the rs9923231 genotype was signif- cantly associated with the daily warfarin dose; each copy of the C allele was associated with a 1.16 mg higher dose of warfarin (p value = 3.55 × 10–45, Table 3) afer adjusting for age, BMI, gender, and smoking status. At a week interval, the warfarin dose was higher by an average of 8.45 mg/week (p value = 4.0 × 10–46) for each additional allele-C copy (Fig. 2).

Time to stable INR. Te Cox regression model indicated a signifcant association of the rs9923231 geno- type with the time to a stable INR (Fig. 2). Patients with the CC genotype had a statistically signifcant delay

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Variables Category Ex-user Naïve All Dif Male 139 (0.32) 301 (0.68) 440 (0.47) Gender 0.014 Female 195 (0.39) 301 (0.61) 496 (0.53) Yes 9 (0.12) 64 (0.88) 73 (0.08) Smoking* 2.32E−06 No 309 (0.38) 506 (0.62) 815 (0.92) Indication Yes 227 (0.54) 191 (0.46) 418 (0.45) Atrial fbrillation 8.53E−27 No 107 (0.21) 409 (0.79) 516 (0.55) Yes 38 (0.22) 133 (0.78) 171 (0.18) Deep vein thrombosis 2.54E−05 No 296 (0.39) 467 (0.61) 763 (0.82) Yes 35 (0.2) 140 (0.8) 175 (0.19) Pulmonary embolism 5.56E−07 No 299 (0.39) 460 (0.61) 759 (0.81) Yes 0 (0) 7 (1) 7 (0.01) Orthopedic surgery 0.0126 No 334 (0.36) 593 (0.64) 927 (0.99) Yes 19 (0.29) 46 (0.71) 65 (0.07) Stroke 0.248 No 315 (0.36) 554 (0.64) 869 (0.93) Yes 47 (0.26) 131 (0.74) 178 (0.19) Others 0.00319 No 287 (0.38) 469 (0.62) 756 (0.81) Co-morbidities Yes 200 (0.39) 308 (0.61) 508 (0.54) Diabetes 0.0101 No 134 (0.31) 294 (0.69) 428 (0.46) Yes 270 (0.39) 419 (0.61) 689 (0.74) Hypertension 1.44E−04 No 64 (0.26) 183 (0.74) 247 (0.26) Yes 144 (0.36) 251 (0.64) 395 (0.42) Hyperlipidemia 0.67 No 190 (0.35) 351 (0.65) 541 (0.58) Yes 3 (0.3) 7 (0.7) 10 (0.01) Hyperthyroidism 0.72 No 331 (0.36) 595 (0.64) 926 (0.99) Yes 50 (0.5) 51 (0.5) 101 (0.11) Hypothyroidism 0.0025 No 284 (0.34) 551 (0.66) 835 (0.89) Yes 41 (0.23) 137 (0.77) 178 (0.19) None 5.74E−05 No 293 (0.39) 465 (0.61) 758 (0.81) Drug interactions Yes 2 (0.13) 13 (0.87) 15 (0.02) Induce CYP2C9 0.0552 No 318 (0.35) 581 (0.65) 899 (0.98) Yes 31 (0.21) 116 (0.79) 147 (0.16) Inhibit CYP2C9 6.41E−05 No 289 (0.38) 478 (0.62) 767 (0.84) Yes 287 (0.38) 467 (0.62) 754 (0.82) None 1.36E−05 No 33 (0.21) 127 (0.79) 160 (0.18) Renal function More than 50 242 (0.36) 423 (0.64) 665 (0.72) GFr, ml/min Between 10 and 50 76 (0.37) 132 (0.63) 208 (0.22) 0.482 Less than 10 16 (0.29) 40 (0.71) 56 (0.06) Stabilized INR Yes 212 (0.32) 454 (0.68) 666 (0.71) Stable INR during 10 days 0.0001 No 122 (0.45) 148 (0.55) 270 (0.29)

Table 1. Te categorical variables of the baseline characteristics of the patients. Bold shows a statistical signifcant diferences between naïve and ex-user group.

in achieving a stable INR, compared to the TT carriers. By day 10, 62% of the CC genotype carriers achieved a stable INR compared to 73% and 75% of the CT and TT carriers, respectively, a statistically signifcant difer- ence (p value = 0.008, fgure). Te median time to achieve a stable INR was 8 days for the CC genotype carriers, compared to 7 days for the carriers of the remaining genotypes. Te HR of CC carriers for not achieving a stable INR during the frst 10 days was 1.33 (95% confdence interval 1.07–1.64, p value = 0.01) compared to the CT carriers and 1.39 (95% CI 1.1–1.71, p value = 0.0052) compared to the TT carriers (Fig. 3).

Warfarin dose. In the subgroup of 602 warfarin naïve patients, the C allele of rs9923231 was signifcantly associated with an average of 1.14 mg higher dose of warfarin (p value = 6.21E−31, Table 3). At day 10, 61% of the CC carriers achieved a stable INR compared to 78% and 82% of the CT and TT carriers, respectively (p

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Ex-user Naive All Variable Mean (SD) n Mean (SD) n Mean (SD) n Dif.a Age, years 67.2 (15.1) 334 59.6 (20) 602 62.3 (18.7) 936 2.60E−09 BMI, kg/m2 31.4 (8.5) 334 29.7 (8.9) 602 30.3 (8.8) 936 0.0036 Average daily dose 3.8 (2) 334 3.5 (2) 602 3.6 (2) 936 0.014 Average weekly dose 24.55 (0.56) 334 26.88 (0.76) 602 25.38 (13.8) 936 0.014 rs9923231—CC 38.63 (1.63) 64 33.71 (1.10) 130 35.28 (0.92) 194 0.05 rs9923231—CT 26.31 (1.02) 163 24.94 (0.77) 261 25.45 (0.62) 424 0.27 rs9923231—TT 20.12 (1.26) 107 18.07 (0.86) 211 18.74 (0.72) 318 0.09 Days to stable INR 5.99 (4.4) 334 7.28 (2.8) 602 6.82 (3.5) 936 8.83E−08 rs9923231—CC 5.68 (4.5) 64 8.18 (2.8) 130 7.38 (3.6) 194 0.000008 rs9923231—CT 5.95 (4.4) 163 7.18 (2.8) 261 6.72 (3.5) 424 0.001 rs9923231—TT 6.19 (4.5) 107 6.82 (2.8) 211 6.62 (3.4) 318 0.13 Coagulation profle INR (3–10 days) 2.2 (0.64) 334 2.27 (0.66) 602 2.25 (0.65) 936 0.09 Prothrombin time, s 22.4 (11.5) 333 13.7 (10) 602 16.8 (11.4) 935 2.46E−31 aPTT, ec 39.7 (14.9) 331 33 (13.3) 602 35.4 (14.3) 933 2.32E−12 Liver function test Total bilirubin, μmol/L 12.2 (9.4) 333 12.3 (9.6) 596 12.3 (9.5) 929 0.98 Albumin, g/L 36 (7.8) 314 33.5 (11) 595 34.4 (10.1) 909 0.0003 ALP, U/L 105.2 (82.8) 333 112 (71.6) 595 109.6 (75.8) 928 0.1862 ALT, U/L 25.6 (33.4) 333 36.8 (119.7) 596 32.8 (98.1) 929 0.0959 AST, U/L 27.7 (31) 334 37.8 (82) 598 34.2 (68.4) 932 0.0316 LDH, U/L 253.7 (149.7) 286 291.3 (164.2) 539 278.2 (160.2) 825 0.0013 GTP, U/L 86.4 (120.9) 247 106.1 (195.5) 491 99.5 (174.3) 738 0.1457 Tyroid function TSH, MIU/L 4.1 (16.5) 318 4.1 (19.2) 551 4.1 (18.3) 869 0.9875 Complete blood counts (CBC) Hematocrit, L/L 1.3 (11.3) 334 3.6 (10.9) 602 2.8 (11.1) 936 0.0017 Hemoglobin, G/L 119.8 (25.2) 334 113.5 (35.4) 602 115.7 (32.3) 936 0.0043 RBC, ­1012/L 6.5 (27.5) 334 7.7 (38.4) 602 7.3 (34.9) 936 0.6125 Platelet, ­109/L 266.6 (123.2) 334 284.4 (124) 602 278.1 (124) 936 0.0356 Renal function test BUN, mmol/L 9.8 (13.1) 334 9.6 (12.2) 602 9.7 (12.5) 936 0.792 Creatinine, μmol/L 138.3 (165.7) 334 136.5 (159.9) 602 137.2 (161.9) 936 0.8728

Table 2. Te continuous variables of the baseline characteristics of the patients. aPTT activated partial thromboplastin time, ALP alkaline phosphatase, ALT alanine amino transferase, AST aspartate amino transferase, BMI body mass index, BUN blood urea nitrogen, RBC red blood cells, TSH thyroid stimulating hormone, LDH lactate dehydrogenase, GTP gamma-glutamyl transferase. a Compare the mean diference for ex-user group to the naïve group by ANOVA. Bold shows a statistical signifcant diferences between naïve and ex-user group.

Genotype frequency Minor Major Minor Allele Efect size Status CHR SNP BP Allele Allele Frequency CC CT TT (mg) p value* Overall 0.43 194 (0.21) 424 (0.45) 318 (0.34) 1.17 3.55E−45 Naive 16 rs9923231 31096368 C T 0.43 130 (0.22) 261 (0.43) 211 (0.35) 1.14 6.21E−31 Ex-user** 0.43 64 (0.19) 163 (0.49) 107 (0.32) 1.23 9.37E−16

Table 3. Genotypic frequency of rs9923231 and association with average weekly dose of warfarin. *Model is adjusted for age, gender, BMI, and smoking status. **Chi Square test for the diference in genotypic frequencies between the initiation and maintenance groups is not signifcant (p value = 0.273). Bold shows a statistical signifcant diferences between naïve and ex-user group.

value = 0.00003, Fig. 4A). Te HR for not achieving a stable INR within the frst 10 days of initiating the warfarin dose was signifcantly higher for the CC carriers by 1.53 (95% confdence interval 1.18–2.00, p value = 0.001)

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Figure 2. Average of warfarin weekly dose by rs29923231 genotype status.

Figure 3. Time to stable INR between the three genotypes.

compared to CT, and by 1.76 (95% confdence interval 1.35–2.32, p value = 0.00005) compared to TT, respec- tively. In the subgroup of 334 maintenance dose patients, the rs9923231-C was also signifcantly associated with an average of 1.24 mg higher dose of warfarin (p value = 9.37E−16, Table 3). However, there were no statistically signifcant diference between the three genotypes in maintaining a stable INR during the frst 10 days of obser- vation (p value = 0.75, Fig. 4B).

Non‑genetic factors. All non-genetic factors were entered in the stepwise multiple regression model to identify the clinical variables signifcantly associated with the warfarin dose. Te stepwise regression identifed seven factors namely, age, weight, status of warfarin, stroke as an indication, the laboratory measurements of red blood cells (RBC), Gamma-Glutamyl Transferase (GTP), and Alkaline phosphatase (Table 4). Tese factors explained 14% of the daily warfarin dose. Te addition of rs9923231 into the model explained an additional 17%, with total of 31% variance explained by the full genetic model (Table 4).

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Figure 4. Number of days to reach a stable INR for (A) Warfarin Naïve group and (B) Warfarin ex-user patients.

Non-genetic mode Genetic model Variable Estimate Std error p value Estimate Std error p value Intercept 4.64 0.40 3.00E−28 3.69 0.38 3.00E−21 rs29923231 C 1.09 0.08 7.00E−35 Age − 0.03 0.00 7.00E−13 − 0.03 0.00 3.00E−15 Weight 0.02 0.00 8.10E−07 0.02 0.00 1.30E−07 Maintenance dose 0.27 0.07 2.20E−04 0.26 0.07 1.00E−04 GTP − 0.001 0.0004 0.01 − 0.001 0.0004 0.02 AlkPhos − 0.002 0.001 0.02 − 0.002 0.001 0.02 RBC 0.005 0.002 0.03 0.004 0.002 0.10 Indication stroke [no.] − 0.25 0.13 0.05 − 0.22 0.11 0.06 R2 0.32 0.15 Adjusted R­ 2 0.31 0.14

Table 4. Stepwise parameters for the impact of non-genetic and genetic models on the warfarin daily dose.

Discussion Te SWAP cohort is the largest cohort of Saudi warfarin using patients with the aim of studying the pharmaco- genetics of warfarin responsiveness. In this relatively large cohort, we found a statistically signifcant association between rs9923231 and the average daily warfarin dose as well as the number of days to achieve a stable INR. Te addition of the rs9923231 genotype to the warfarin dose prediction model, that included non-genetic factors, doubled the explained variation to 31%. Te rs9923231 was a statistically signifcant factor that determined the number of days required to achieve a stable INR when initiating warfarin, but this signifcance was not found with the group receiving a maintenance dose.

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Te role of genetic variants within VKORC1 is known and have been incorporated in genotype-based dosing algorithms in several ­populations13. Essentially, warfarin antagonizes the Vitamin K-dependent clotting pathway, in which the VKORC1 gene product, VKORC1 protein, is the rate-limiting step in Vitamin K recycling­ 26. As the rs9923231 is located in the promoter region of VKORC1, it alters the promoter activity and the transcription factor binding site, leading to a reduction by almost 44% in the luciferase activity of the T allele compared to the C allele­ 27. Tis polymorphism is the most prevalent polymorphism in VKORC1 associated with the warfarin dose and sensitivity­ 13,28,29. Te International Warfarin Pharmacogenetics Consortium (IWPC) recommend reducing the weekly warfarin dose for carriers of the C ­allele23. Limited studies investigated the impact of the pharmacogenetic efect on warfarin treatment in Saudi patients. A study with 112 Saudi patients reported an allele frequency estimate of 45% for allele C, similar to the current study­ 30. Other studies were conducted with healthy volunteers to estimate the allelic frequency­ 31,32. Comparing 499 healthy Saudi participants to 1,105 Europeans, and 106 South Africans, indicated the allele frequencies for the C allele as 0.46, 0.42, and 0.36, respectively­ 31. Based on the allele frequencies, this study predicted a statistically signifcant diference in the warfarin dose between the three populations, with the Saudi population having an average warfarin dose equal to 35.38 mg/week [95% confdence intervals 35.23–36.44, compared to 37.88 mg/ week [95% CI 37.41–38.36] and 34.83 mg/week [95% CI 32.41–37.26] for the European and South African populations, respectively. Te IWPC dosing guideline recommends decreasing the average dose of warfarin for patients with the rs9923231 TT and CT genotypes by − 16.14 mg and − 8.97 mg/week, respectively, compared to patients with similar characteristics but a carrier of the CC ­genotype23. In our study, the average of warfarin dose for TT and CT carriers was lowered by − 16.54 and − 9.83 mg/week, respectively. Although these estimates are based solely on the VKORC1 status with no consideration of other important factors such as the CYP2C9 genotype, it indicates that patients treated by the traditional dosing method, have converged to groups with an average dose close to the IWPC dosing guidelines. Tis does not imply that the full dosing algorithm can be applied in the Saudi population, as other factors could be diferent between the populations. Other rare variants within VKORC1 and other important pharmacogenes (VIP) could play major roles in populations ­diferences33. It must be considered that the clinical value of a genotype-based dosing algorithm, over the standard dosing approach, is still unclear­ 25,34,35. To determine whether this result will translate into signifcant clinical benefts or whether the work describes interesting rare genetic variants remains to be examined. In this study, we achieved a relatively large sample size of warfarin patients from an under-represented population. Te SWAP cohort provides a valuable resource for future studies to develop genotype-based dosing algorithms, targeting the Saudi population. Although we only tested the pharmacogenetic impact of VKORC1 on the warfarin response, the study serves as proof of concept to justify future research to assess the full spectrum of genetic variants using advanced technologies, such as Next-generation Sequencing. One limitation of this study is the fact that we followed-up the patients for 10 days only. During which, it is difcult to draw an informative interpretation about the quality of clinical setting in patients, taking into consideration that the majority of our patients have never been exposed to warfarin at the time of enrollment in the study. Nevertheless, longer follow- up analysis will be conducted in future studies by following the patient electronic health record. In conclusion, SWAP represents an unprecedented resource as the largest national cohort of warfarin using patients to support a range of studies, with the ultimate goal of identifying rare and prevalent variants to develop personalized anticoagulation treatment. We reported the association of the VKORC1 promoter variant with the warfarin dose and time to a stable INR in the Saudi population. Te fndings establish a baseline for additional studies to assess the impact of genotype-guided warfarin dosing by using the SWAP materials.

Received: 3 February 2020; Accepted: 25 June 2020

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Author contributions M.A.A., M.A.B., I.B.A., B.A., and J.A. contributed to study concept and design; K.S. and N.S.A. acquired clinical data; M.A.D., B.A., and J.A. designed the genotyping assay and experimentation; M.A.A., B.A., and J.A. inter- preted data; M.A.A and J.A. statistical analysis; M.A.A., K.S. and J.A. drafed and revised manuscript.

Competing interests Te authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to J.A. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations.

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